Instructional technology provides the capacity to address the needs of students with diverse cognitive skills and socialization needs. Learning experience is viewed as an important factor in learner engagement/motivation, and a contributor to learning in online instruction (Sims, 2003; Swartzwelder & Murphy, 2019; Chan, Wan & Ko, 2019). Moore’s three types of learning interaction (Moore, 1989) included student-content interaction, student-student interaction, and student-faculty interaction; and have been used widely in the research literature. Several studies have demonstrated that well-designed online interactivities can improve student’s learning experience (Svihla, 2015; Cain & Lee, 2016; Watkins, 2005; Herrington, Oliver & Reeves, 2003). However, the field has no clear agreement on how to measure these interactivities for improving learning experience in online instruction (Ekwunife-Orakwue and Teng, 2014; Walmsley-Smith, Machin and Walton, 2019). Some assume that an analytics approach, using tracking data from behavioral and physiological responses (e.g., facial expressions, eye tracking, click-stream data) as evidence of involvement and attentiveness, is a measure of motivation and engagement. Using the physiological response data in online instruction can be a reliable source of understanding online activities that enhance learning experience (Lee & Shapiro, 2019; Lee & DuMont, 2010). The purpose of this project is to explore how to design learning activities in hands-on lessons online that are effective and engaging based on facial expressions and physiological responses.
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